基于长沙市T区社区管理模式现状的思考

以长沙市T区为分析案例,通过对T区进行调查研究,寻找当前长沙市社区管理模式存在的一些问题:管理体制的行政化,政府职能的错位与缺位,居民与社区中介组织参与社区管理程度低,社区工作任务繁重,等等。要解决当前社区管理存在的一系列问题,必须重新考析政府、社区、社区中介组织以及居民之间的关系,理顺社区管理体制,明确政府职能,大力培育社区中介组织,培养居民的参与意识,构建长沙市功能型社区管理模式。     

裂缝地层漏失规律研究

本文根据地下裂缝的漏失特性，建立了地下裂缝漏失规律的数学模型，并导出了各种流体在裂缝中的流动挽律。根据流动规律可以定量分析和计算漏速或漏失量与压差、裂缝尺寸、钻井液流变性能等之间的关系，这时优选优配堵漏荆的性能，选择合理的堵漏方法，提高防漏堵漏的成功率具有重要的指导意义。     

Composite Estimation for Single‐Index Models with Responses Subject to Detection Limits

We propose a semiparametric estimator for single‐index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non‐parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy‐tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.     

湖南省学前教育普及现状调查与对策建议

湖南省实施发展学前教育三年行动计划效果明显，学前教育机构总数、在园幼儿数、学前三年毛入学率显著增加。与经济发达省份对比，湖南省学前教育普及水平还比较落后，城镇学前教育普及水平明显高于农村地区，县域之间学前教育普及水平差距明显。湖南省普及学前教育面临的主要问题是：政府主导作用发挥不够、农村学前教育普及水平落后、学前教育基础建设不足、普及学前教育路径不畅。推进湖南学前教育持续普及，需要构建学前教育公共服务体系，大力扶持农村学前教育，建立学前教育的保障体系，优化学前教育普及模式。     

On additive-multiplicative hazards model

In survival analysis and reliability theory, a fundamental problem is the study of lifetime properties of a live organism or system. In this regard, there have been considered and studied several models based on different concepts of ageing such as hazard rate and mean residual life. In this paper, we consider an additive-multiplicative hazard model (AMHM) and study some reliability and ageing properties of the proposed model. We then specify the bivariate models whose conditionals satisfy AMHM. Several properties of the proposed bivariate model are investigated and adequacy of the model is evaluated based on a real data set.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Foodborne Exposure to Pesticides and Methylmercury in the United Arab Emirates

As part of a comprehensive environmental health strategic planning project initiated by the government of Abu Dhabi, we assessed potential dietary exposure in the United Arab Emirates (UAE) to methylmercury (in seafood) and pesticides (in fruits and vegetables) above international guideline levels. We present results for the UAE population by age, gender, and body mass index. Our results show very low daily risks of exposure to pesticides in fruits and vegetables at levels exceeding WHO guidelines even under the conservative assumption that no pesticides are removed during washing and food preparation. Thus, exposure to pesticides on fruits and vegetables does not appear to be a major public health concern in the UAE. The chances of exposure to methylmercury in seafood are much higher; our model estimates a mean 1 in 5 daily risk of exceeding the FAO/WHO provisional tolerable weekly intake. However, great caution should be used in interpreting these results, as we analyzed only the risks and not the substantial benefits of fish consumption. In fact, previous studies have demonstrated that exposure to the n‐3 polyunsaturated fatty acids in fish can increase IQ in developing children, and it can substantially decrease the risk in adults of coronary heart disease and stroke. Further research is warranted to compare the risk of Me‐Hg exposure from fish to the nutritional benefits of fish consumption in the UAE and to determine appropriate methods to communicate risk and benefit information to the UAE population.     

中国决策情境下的多源流模型及其优化研究

作为当今公共政策研究领域最炙手可热的理论模型之一,约翰·金登的多源流分析理论业已得到国内外学者的广泛应用与认可。在对该模型的相关内容进行简单铺陈后,通过辨析国内外不同的政策主体参与逻辑,进一步厘清了我国公共决策的独有特征。研究重点是在整合提炼西方既有修正思路的基础上,沿袭模型的模糊性假设,结合我国具体实践案例,对多源流模型进行优化与补充。研究表明,中国决策情境下的多源流模型优化主要有提高源流间的融合性、政治源流的核心化、焦点事件的独立化以及特殊情况下的临时决策等四种路径,四种优化与补充路径的有机结合将为进一步提升模型适用性提供帮助。     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

Bivariate versus univariate ordinal categorical data with reference to an ophthalmologic study

The Wisconsin Epidemiologic Study of Diabetic Retinopathy is a population-based epidemiological study carried out in Southern Wisconsin during the 1980s. The resulting data were analysed by different statisticians and ophthalmologists during the last two decades. Most of the analyses were carried out on the baseline data, although there were two follow-up studies on the same population. A Bayesian analysis of the first follow-up data, taken four years after the baseline study, was carried out by Angers and Biswas [Angers, J.-F. and Biswas, A., 2004, A Bayesian analysis of the four-year follow-up data of theWisconsin epidemiologic study of diabetic retinopathy. Statistics in Medicine, 23, 601–615.], where the choice of the best model in terms of the covariate inclusion is done, and estimates of the associated covariate effects were obtained using the baseline data to set the prior for the parameters. In the present article we consider an univariate transformation of the bivariate ordinal data, and a parallel analysis with the much simpler univariate data is carried out. The results are then compared with the results of Angers and Biswas (2004). In conclusion, our analyses suggest that the univariate analysis fails to detect features of the data found by the bivariate analysis. Even an univariate transformation of our data with quite high correlation with both left and right eyes is inadequate.